I was at Tech Arena last week. This is one of the biggest tech events in the Nordics - a place to catch the leading trends, talk to startups, investor, politicians, and users. Almsot needless to say, AI was top of the agenda. Some difficult questions were being aksed, but maybe not enough. Maybe there was too much of a positive vibe.
It made me think on one of the most interesting and multifaceted Leadership Principles. Have Backbone, Disagree and Commit. It reads like this.
Have Backbone; Disagree and Commit. "Leaders are obligated to respectfully challenge decisions when they disagree, even when doing so is uncomfortable or exhausting. Leaders have conviction and are tenacious. They do not compromise for the sake of social cohesion. Once a decision is determined, they commit wholly."
It is an especially important principle when everyone seems to think alike. When the culture is that of acceptance and even consensus (like is also a common belief about Sweden - where I live and work).
We can think of this principles in many different ways in the age of AI. I'll touch on some of them, and will revisit it in future issues.
The age of AI obligates us to rethink common beliefs
The abundance of intelligence is undermining many of the things we have been taking for granted. That things have to be done in certain ways. That some things are more important than others. I am having more and more conversations with leaders who are starting to ask themselves and their teams fundamental questions about how and why things are done.
In addition, the process of looking into our workflows as part of the use case discovery for AI, often reveals that we are not really acting the way that we claim to. That we are not actually following those best practices we supposedly put in place. That a lot of it has been theater for others, or worse yet - fooling ourselves.
A few exampes - from marketing, to engineering, to organizational structures
To make this more concrete, here are a few examples of rethinking common paradigms that came up at Tech Arena conversations I have had.
1. Is the marketing practice broken?
Marketing professionals have been optimizng their digital assets for search engines for more than 2 decades. Tools and best practices have been built. Content has been written in certain ways, and roles and teams with specific capabilities and skills have established.
But brand and product discovery is changing quickly. I touched on this in issue 1 of this newsletter, discussing the term Answer Engine Optimization. In a session by Trust Pilot, it was quite obvious that: a. No one really has a playbook for this now. b. It will keep changing quickly with the rapid evolution of AI models and products, and c. You need to write not just for humans anymore, but also for AI agents.
If you are in marketingm, you definitely need to have backbone and challenge many of your assumptions.
2. Will engineering ever be the same?
2025 has been the year of AI coding agents. It will only accelerate, as many of the leading players see AI powered software as an unlock for other use cases. This has been the focus of the latest model releases from OpenAI, Anthropic, Google, X.ai, and others. We have also seen tools such as Cursor, Claude Code, and Codex sweaping the stage. The role of the software engineer is becoming more that of an AI agent orchestrater and architect.
3. Rapid innovation, standards, and hackathons
I facilitated a panel at Tech Arena, titled How do you turn AI hype and one-off hackathons into real, repeatable innovation? In it, we were debating if innovation current practices and standards are still relevant in the age of AI, and what "truths" should we hold on to when driving innovation, and which structures, roles and responsibilites have to be reshaped. I must say - I don't think we have good answers for these questions.
Tech Arena panel on AI, Innovation and Hackathons
Your action step
Pick one established process in your organization - marketing, engineering, innovation, hiring, whatever feels most "settled." Block 30 minutes with the people closest to it and ask two questions:
- Are we actually doing this the way we claim?
- If we started from scratch today, with AI as a given, would we design it the same way?
If the answer to either is no, you've found where backbone is needed.
Then flip the lens. Look at where your team is adopting AI, and ask:
- Are we doing this because it genuinely solves our problem, or because everyone else is doing it?
- Are we adding a chatbot because customers need one, or because it's the default AI feature?
- Are we rushing to automate a workflow that we don't fully understand yet?
The same backbone that should push us to rethink old processes should also push us to question new ones. Sometimes having backbone means saying "not yet" or "not like this" - even when the rest of the industry is racing ahead.